Simple_Linear_Regression_in_ML.ipynb

simple_linear_regression_in_ml.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNKa/EhPWjbAiLxLYxjtciE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/hemantramphul/c8e8e3010dcea334c1852166e6fa2461/simple_linear_regression_in_ml.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Simple Linear Regression**"
      ],
      "metadata": {
        "id": "CBwSUHFCEsz-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing the libraries"
      ],
      "metadata": {
        "id": "Z84_0_ytEwfA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E3e5Shfa2P8k"
      },
      "outputs": [],
      "source": [
        "import numpy as np  # NumPy for numerical operations\n",
        "import matplotlib.pyplot as plt  # Matplotlib for plotting\n",
        "import pandas as pd  # Pandas for data manipulation\n",
        "import statsmodels.api as sm  # Statsmodels for statistical modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing the dataset"
      ],
      "metadata": {
        "id": "c6mhTWj0E3Bi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import a CSV file using the read_csv() function from the pandas library.\n",
        "# Read data from a URL with the pandas.read_csv()\n",
        "\n",
        "# Load the dataset from the specified CSV file\n",
        "data_set= pd.read_csv('../content/sample_data/salary_data.csv')\n",
        "\n",
        "# Create a scatter plot using the 'YearsExperience' as x-axis and 'Salary' as y-axis\n",
        "data_set.plot(x=\"YearsExperience\", y=\"Salary\", kind=\"scatter\")"
      ],
      "metadata": {
        "id": "qHLyYdXu2viQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "cfaefd20-f8df-4f3d-d51c-8236e0b4589f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='YearsExperience', ylabel='Salary'>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the linear regression model\n",
        "ey = data_set['Salary']  # Dependent variable\n",
        "ex = data_set['YearsExperience']  # Independent variable\n",
        "ex = sm.add_constant(ex)  # Adding a constant term for the intercept\n",
        "linear_model = sm.OLS(ey, ex)  # Ordinary Least Squares (OLS) method for linear regression\n",
        "\n",
        "# Fit the linear regression model\n",
        "lin_result = linear_model.fit()\n",
        "\n",
        "# Print the summary of regression results\n",
        "print(lin_result.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqhGAWN1BGn0",
        "outputId": "18eb6e3e-7de8-44a4-c9bf-3f9b0418f04d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                 Salary   R-squared:                       0.957\n",
            "Model:                            OLS   Adj. R-squared:                  0.955\n",
            "Method:                 Least Squares   F-statistic:                     622.5\n",
            "Date:                Mon, 08 Jan 2024   Prob (F-statistic):           1.14e-20\n",
            "Time:                        08:14:15   Log-Likelihood:                -301.44\n",
            "No. Observations:                  30   AIC:                             606.9\n",
            "Df Residuals:                      28   BIC:                             609.7\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===================================================================================\n",
            "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------\n",
            "const            2.579e+04   2273.053     11.347      0.000    2.11e+04    3.04e+04\n",
            "YearsExperience  9449.9623    378.755     24.950      0.000    8674.119    1.02e+04\n",
            "==============================================================================\n",
            "Omnibus:                        2.140   Durbin-Watson:                   1.648\n",
            "Prob(Omnibus):                  0.343   Jarque-Bera (JB):                1.569\n",
            "Skew:                           0.363   Prob(JB):                        0.456\n",
            "Kurtosis:                       2.147   Cond. No.                         13.2\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract independent variable (X) and dependent variable (y) from the dataset\n",
        "x = data_set.iloc[:, :-1].values  # Independent variable (all columns except the last one)\n",
        "y = data_set.iloc[:, 1].values  # Dependent variable (second column)\n",
        "\n",
        "# Uncomment the line below to print the values of the dependent variable (y)\n",
        "# print(y)"
      ],
      "metadata": {
        "id": "Quz2vmUJ4KMj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Splitting the dataset into the Training set and Test set"
      ],
      "metadata": {
        "id": "6zCNi0DLFK9D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the train_test_split function from sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=1/3, random_state=0)\n"
      ],
      "metadata": {
        "id": "Nvk-x4EE5B0V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training the Simple Linear Regression model on the Training set"
      ],
      "metadata": {
        "id": "Jlch0f3YFQ-n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the LinearRegression class from sklearn\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Create a linear regression model\n",
        "regressor = LinearRegression()\n",
        "\n",
        "# Fit the model using the training data\n",
        "regressor.fit(x_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "XQhg-4Mm5NRl",
        "outputId": "798e203b-0340-4aa9-e5fa-5a087ebfad4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Predicting the Train and Test set results\n"
      ],
      "metadata": {
        "id": "Qm6NE3pQFW3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained model to make predictions on the test set\n",
        "y_pred = regressor.predict(x_test)\n",
        "\n",
        "# Use the trained model to make predictions on the training set (optional)\n",
        "x_pred = regressor.predict(x_train)\n"
      ],
      "metadata": {
        "id": "m0NbYmpL5Vnr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualising the Training set results"
      ],
      "metadata": {
        "id": "xbdZLOdfFplX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the training data points and the linear regression line\n",
        "plt.scatter(x_train, y_train, color=\"green\", label=\"Actual Data\")\n",
        "plt.plot(x_train, x_pred, color=\"violet\", label=\"Linear Regression Line\")\n",
        "plt.title(\"Salary vs Experience (Training Dataset)\")\n",
        "plt.xlabel(\"Years of Experience\")\n",
        "plt.ylabel(\"Salary (in MUR)\")\n",
        "plt.legend()  # Show legend for clarity\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "XBOztPjj5YJM",
        "outputId": "77eecdfd-9a45-4590-e8ea-7a76de9a63c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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4IxYtWoRJkybho48+suIKWiczM9M0Kfzy5cumJDG/cq/5a6+9lmcC9PCdb0De19ZSu3PnzjWb75Yrt/ciV17LCOQnwck976pVq0xfmg970nIDuV/oSUlJ8Pf3t/q8uSxdm48//hgTJ07EG2+8gWnTpqFMmTKQy+WIjIx87Gc+19Msr2DN6582cXwaZ8+eRYUKFUwJSkGvicFgwEsvvYS7d+9i7NixqFmzJtzd3XHjxg0MHDhQ0kZkZCS6dOmCjRs3Yvv27Zg4cSJmzpyJXbt2oUGDBgX+zDwsN7krV66c1a8h22JSRSVW48aNAQA3b94EAGzevBmZmZnYtGmT5K9na7rXN2zYgGeeeca07k2uyZMn5yumOnXqoEGDBli9ejX8/f0RHx9v9QKlLVu2hK+vL9auXYsXXngBu3btwoQJE8zqubu749VXX8Wrr76KrKwsdO/eHTNmzMD48eNtdtv75MmTERsbi08++QRjx47FuHHjsGDBArN6uX+FP+zvv/+Gm5ubqffD09MTBoMh33e8PU6VKlUA3P8r31btVqlSBUajEefPn88zUcs9b4UKFZ7qvDVr1gQAxMXFoW7duk8d68M2bNiANm3a4Ouvv5aUJycnO8SXb6VKlWA0GhEXFyfp2Xya9eUsOXToEC5fviwZYrf2muTV43PmzBn8/fffWLlyJfr3728qf3gy/MOqVKmC0aNHY/To0bh48SLq16+PefPm4bvvvsvXZ+ZJPVBxcXEA7v9xRfbBOVVU7O3evdviX725WzbUqFEDwIO/mB+uq9PpsHz58ieew9JrDx8+jEOHDuU73tdffx2//vor5s+fj7Jly6JDhw5WvU4ul6Nnz57YvHkzVq1ahZycHMnQH/BgTk4upVKJWrVqQQghGbIsiMOHD+OTTz5BZGQkRo8ejffeew8LFy7E3r17zeoeOnRIMkfl2rVr+Pnnn9GuXTsoFAooFAr06NEDP/74I86ePWv2+v/++++pYmzUqBGqVKmCTz75xOJWL0/Tbnh4OORyOaZOnWrWm5H7uQgLC4NarcbHH39s8Xo/6byNGjWCUqnEsWPH8h1fXhQKhdn/j/Xr1+PGjRs2O0dBhIWFAQAWLVokKbfFbghXr17FwIEDoVQq8d5775nKrb0m7u7uAO4nWw+z9PtACIGoqChJvfT0dGRkZEjKqlSpAk9PT2RmZgLI32cmr3hyHT9+HBqNxqEX6i3p2FNFxd7IkSORnp6Obt26oWbNmsjKysLBgwexdu1aBAUFmdZ3ateuHZRKJbp06YK33noLqamp+Oqrr1ChQgVTb1ZeOnfujOjoaHTr1g2dOnVCXFwclixZglq1auV7f7a+ffvi/fffx08//YRhw4bB2dnZ6te++uqr+PzzzzF58mTUrVvX7C/Sdu3awcfHBy1atIC3tzdiY2OxcOFCdOrUyTSf5HFOnDiB7777zqy8SpUqaNasGTIyMjBgwABUq1YNM2bMAHB/mYHNmzdj0KBBOHPmjOkXP3C/Zy4sLEyypELua3LNmjULu3fvRpMmTTBkyBDUqlULd+/exYkTJ/Dbb7/h7t27Vl+fXHK5HMuWLUOHDh1Qu3ZtDBo0CBUrVsSNGzewe/duqNVqbN68OV9tVq1aFRMmTMC0adMQEhKC7t27Q6VS4ejRo/Dz88PMmTOhVquxePFivP7662jYsCF69+6N8uXLIz4+Hlu3bkWLFi2wcOHCPM/h4uKCdu3a4bfffsPUqVPz/b4t6dy5M6ZOnYpBgwahefPmOHPmDFavXo1nnnnGJu0XVKNGjdCjRw/Mnz8fd+7cMS2p8PfffwOwfn5Q7mfXaDQiOTkZR48exY8//giZTIZVq1ZJhpGtvSZVqlSBVqvFkiVL4OnpCXd3dzRp0gQ1a9ZElSpVMGbMGNy4cQNqtRo//vij2dyqv//+G23btkWvXr1Qq1YtODk54aeffkJiYqJpGZT8fGZyb/oYNWoUwsLCoFAoJCvF79ixA126dOGcKnsq+hsOiWxr27Zt4o033hA1a9YUHh4eQqlUiqpVq4qRI0eKxMRESd1NmzaJevXqCRcXFxEUFCRmz54tvvnmGwFAxMXFmeo9uqSC0WgUH3/8sahUqZJQqVSiQYMGYsuWLWLAgAGiUqVKpnrW3N4txP2lEACIgwcP5uu9Go1GERAQIACI6dOnmx3/8ssvRcuWLUXZsmWFSqUSVapUEe+9957pVvK8PGlJhQEDBgghhHjnnXeEQqEQhw8flrz+2LFjwsnJSQwbNsxUBkAMHz5cfPfdd6JatWqm65a77MHDEhMTxfDhw0VAQIBwdnYWPj4+om3btmLp0qWmOrnLBqxfv97s9Y8uqZDrzz//FN27dzddj0qVKolevXqJnTt3murk3vr/33//SV67fPlys8+FEEJ88803okGDBkKlUgkvLy/RqlUrsWPHDrN4wsLChEajES4uLqJKlSpi4MCBkuUl8hIdHS1kMpmIj4+3ePxxSypYujYZGRli9OjRwtfXV7i6uooWLVqIQ4cOmX3G81pSwd3d3azN3Gv2sEdjys91TUtLE8OHDxdlypQRHh4eIjw8XFy4cEEAELNmzbJ4HR6NO/fh5OQkypQpI5o0aSLGjx9vttRHfq6JEEL8/PPPolatWsLJyUlyfc6fPy9CQ0OFh4eHKFeunBgyZIg4deqUpM7t27fF8OHDRc2aNYW7u7vQaDSiSZMmYt26dWYxWfOZycnJESNHjhTly5cXMplM8jOIjY0VAMRvv/322OtFhYt7/xHZQbdu3XDmzBmbzRtxRDKZDMOHD39szwyZMxgMqFWrFnr16oVp06bZOxy7OXnyJBo0aIDvvvsuz4Vx6YHIyEjs27cPx48fZ0+VHXFOFVERu3nzJrZu3YrXX3/d3qGQA1IoFJg6dSq++OKLfA8tF1ePbu0CAPPnz4dcLkfLli3tEFHxcufOHSxbtgzTp09nQmVnnFNFVETi4uJw4MABLFu2DM7OznjrrbfsHRI5qNy7N0uLOXPm4Pjx42jTpg2cnJywbds2bNu2DUOHDkVAQIC9w3N4ZcuWLTUJuKNjUkVURPbu3YtBgwYhMDAQK1eutLgmDVFp1Lx5c+zYsQPTpk1DamoqAgMDMWXKFItLhhA5Ms6pIiIiIrIBzqkiIiIisgEmVUREREQ2wDlVRchoNOLff/+Fp6cn79AgIiIqJoQQSElJgZ+fH+TyvPujmFQVoX///Zd3shARERVT165de+xm50yqilDuNiHXrl0z7ZZOREREjk2v1yMgIOCJ230xqSpCuUN+arWaSRUREVEx86SpO5yoTkRERGQDTKqIiIiIbIBJFREREZENcE6VAzIYDMjOzrZ3GEQOT6lUPvb2ZiKiosSkyoEIIZCQkIDk5GR7h0JULMjlclSuXBlKpdLeoRARMalyJLkJVYUKFeDm5sYFQokeI3cx3Zs3byIwMJD/X4jI7phUOQiDwWBKqMqWLWvvcIiKhfLly+Pff/9FTk4OnJ2d7R0OEZVynIzgIHLnULm5udk5EqLiI3fYz2Aw2DkSIiImVQ6HQxhE1uP/FyJyJEyqiIiIiGyASRWVaDKZDBs3brR3GEREVAowqSKbOHToEBQKBTp16pTv1wYFBWH+/Pm2D8oKAwcOhEwmg0wmg7OzM7y9vfHSSy/hm2++gdFozFdbK1asgFarLZxAiYjI4TGpKmEMRgP2XNmD7898jz1X9sBgLJoJvF9//TVGjhyJffv24d9//y2Sc9pK+/btcfPmTVy5cgXbtm1DmzZtEBERgc6dOyMnJ8fe4RERkZVEtrDr+ZlUlSDRsdEIigpCm5Vt0De6L9qsbIOgqCBEx0YX6nlTU1Oxdu1aDBs2DJ06dcKKFSvM6mzevBnPPfccXFxcUK5cOXTr1g0A0Lp1a1y9ehXvvPOOqccIAKZMmYL69etL2pg/fz6CgoJMz48ePYqXXnoJ5cqVg0ajQatWrXDixIl8x69SqeDj44OKFSuiYcOG+OCDD/Dzzz9j27Ztkvfy6aefom7dunB3d0dAQAD+97//ITU1FQCwZ88eDBo0CDqdzvQ+pkyZAgBYtWoVGjduDE9PT/j4+KBv3764detWvuMkIiLLchJykDQtCcmzkpHzr/3+GGZSVUJEx0aj57qeuK6/Lim/ob+Bnut6FmpitW7dOtSsWRM1atTAa6+9hm+++QZCPPhrYevWrejWrRs6duyIP//8Ezt37sTzzz9/P+7oaPj7+2Pq1Km4efMmbt68afV5U1JSMGDAAPz+++/4448/UK1aNXTs2BEpKSkFfk8vvvginn32WURHP7hucrkcCxYswLlz57By5Urs2rUL77//PgCgefPmmD9/PtRqtel9jBkzBsD95TKmTZuGU6dOYePGjbhy5QoGDhxY4BiJiAi4t+seUr568HtfprTfXcFc/LMEMBgNiIiJgIB5t6eAgAwyRMZEomuNrlDIFTY//9dff43XXnsNwP2hNJ1Oh71796J169YAgBkzZqB379746KOPTK959tlnAQBlypSBQqEw9eLkx4svvih5vnTpUmi1WuzduxedO3cuwDu6r2bNmjh9+rTpeWRkpOnfQUFBmD59Ot5++20sWrQISqUSGo0GMpnM7H288cYbpn8/88wzWLBgAZ577jmkpqbCw8OjwHESEZVGIkcgeWaypMz9FXcoytn+e85a7KkqAfbH7zfroXqYgMA1/TXsj99v83NfuHABR44cQZ8+fQAATk5OePXVV/H111+b6pw8eRJt27a1+bkTExMxZMgQVKtWDRqNBmq1GqmpqYiPj7dJ+0IIyTpIv/32G9q2bYuKFSvC09MTr7/+Ou7cuYP09PTHtnP8+HF06dIFgYGB8PT0RKtWrQDAZnESEZU2Of/mmCVUmtEaKGvadx9Q9lSVADdTrBsys7Zefnz99dfIycmBn5+fqUwIAZVKhYULF0Kj0cDV1TXf7crlcskQIvBg1flcAwYMwJ07dxAVFYVKlSpBpVKhWbNmyMrKero384jY2FhUrlwZAHDlyhV07twZw4YNw4wZM1CmTBn8/vvvGDx4MLKysvJcCT8tLQ1hYWEICwvD6tWrUb58ecTHxyMsLMxmcRIRlSbpv6Yj83Cm6blzNWd49HaMXn/2VJUAvp6+Nq1nrZycHHz77beYN28eTp48aXqcOnUKfn5++P777wEA9erVw86dO/NsR6lUmm0zUr58eSQkJEgSq5MnT0rqHDhwAKNGjULHjh1Ru3ZtqFQq3L592ybvbdeuXThz5gx69OgB4H5vk9FoxLx589C0aVNUr17d7C5HS+/jr7/+wp07dzBr1iyEhISgZs2anKRORPQURI5A0rQkSULl0dvDYRIqgElViRASGAJ/tT9ksDw5TwYZAtQBCAkMsel5t2zZgqSkJAwePBh16tSRPHr06GEaApw8eTK+//57TJ48GbGxsThz5gxmz55taicoKAj79u3DjRs3TElR69at8d9//2HOnDm4fPkyvvjiC2zbtk1y/mrVqmHVqlWIjY3F4cOH0a9fv6fqFcvMzERCQgJu3LiBEydO4OOPP0bXrl3RuXNn9O/fHwBQtWpVZGdn4/PPP8c///yDVatWYcmSJZJ2goKCkJqaip07d+L27dtIT09HYGAglEql6XWbNm3CtGnT8h0jEVFplnPNwnDfexo4V3OsjdSZVJUACrkCUe2jAMAsscp9Pr/9fJtPUv/6668RGhoKjUZjdqxHjx44duwYTp8+jdatW2P9+vXYtGkT6tevjxdffBFHjhwx1Z06dSquXLmCKlWqoHz58gCA4OBgLFq0CF988QWeffZZHDlyxHQ33cPnT0pKQsOGDfH6669j1KhRqFChQr7fR0xMDHx9fREUFIT27dtj9+7dWLBgAX7++WcoFPev2bPPPotPP/0Us2fPRp06dbB69WrMnDlT0k7z5s3x9ttv49VXX0X58uUxZ84clC9fHitWrMD69etRq1YtzJo1C5988km+YyQiKq3SfklDyooHd/c5BzvDa6IX5C6Ol8LIxKMTV6jQ6PV6aDQa6HQ6qNVqybGMjAzExcWhcuXKcHFxear2o2OjERETIZm0HqAOwPz289E9uHuBYidyRLb4f0NEjklkCSTPTpaUefTzgPMz5r1TBqMB++P342bKTfh6+iIkMMSmHQmP+/5+GCeqlyDdg7uja42uhfrBIiIiKmzZV7OR+m2qpEz7vhYylfk0F0sdCv5qf0S1jyryDgUmVSWMQq5A66DW9g6DiIjoqaT9nIas0w/ujlbWU8K9q7vFurkLXz+6TmPuwtcbem0o0sTK8QYkiYiIqNQRmffv7ns4ofLo75FnQvWkha8BIDImssj2wAWYVBEREZGdZf+TjeQ5yZIy7VgtnCvlfXefPRe+zguH/4iIiMhuUjekIjv2weLOyoZKuHey3Dv1MHsufJ0XJlVERERU5IwZRujm6iRlngM94RRgXWpir4WvH4fDf0RERFSksi9mmyVU2nFaqxMqwH4LXz+OXZOqffv2oUuXLvDz84NMJsPGjRtNx7KzszF27FjUrVsX7u7u8PPzQ//+/c22Brl79y769esHtVoNrVaLwYMHIzVVehvm6dOnERISAhcXFwQEBGDOnDlmsaxfvx41a9aEi4sL6tati19++UVyXAiBSZMmwdfXF66urggNDcXFixdtdzGIiIhKgdQfUpH6w4PvadXzKnhN9ILM2XJylBd7LXz9OHZNqtLS0vDss8/iiy++MDuWnp6OEydOYOLEiThx4gSio6Nx4cIFvPzyy5J6/fr1w7lz57Bjxw5s2bIF+/btw9ChQ03H9Xo92rVrh0qVKuH48eOYO3cupkyZgqVLl5rqHDx4EH369MHgwYPx559/Ijw8HOHh4Th79qypzpw5c7BgwQIsWbIEhw8fhru7O8LCwpCRkVEIV4aIiKhkMaYbkTQtCdkXH8yf8hzsCbcwyxvSW6N7cHds6LUBFdUVJeX+av8iX04BACAcBADx008/PbbOkSNHBABx9epVIYQQ58+fFwDE0aNHTXW2bdsmZDKZuHHjhhBCiEWLFgkvLy+RmZlpqjN27FhRo0YN0/NevXqJTp06Sc7VpEkT8dZbbwkhhDAajcLHx0fMnTvXdDw5OVmoVCrx/fffW/0edTqdACB0Op3ZsXv37onz58+Le/fuWd1ecWDNz5Vsb/LkyeLZZ5+1awy7d+8WAERSUlKhnaOk/r8hKmkyYzPF3al3JQ9jttFm7ecYcsTuuN1izek1YnfcbpFjyLFZ20I8/vv7YcVqTpVOp4NMJoNWqwUAHDp0CFqtFo0bNzbVCQ0NhVwux+HDh011WrZsCaVSaaoTFhaGCxcuICkpyVQnNDRUcq6wsDAcOnQIABAXF4eEhARJHY1GgyZNmpjqWJKZmQm9Xi95lDQDBw5EeHh4nsdv3ryJDh06FF1A+SSTyUwPtVqN5557Dj///LO9wyqwMWPGYOfOnYV+nqCgIMyfP9/isebNm+PmzZsW94YkotIjZVUK0tanmZ67NHe5P9znlL/hvsfJXfi6T90+aB3U2m47iRSbpCojIwNjx45Fnz59TPvuJCQkmG2g6+TkhDJlyiAhIcFUx9vbW1In9/mT6jx8/OHXWapjycyZM6HRaEyPgICAfL3nksDHxwcqlcquMQghkJOTk+fx5cuX4+bNmzh27BhatGiBnj174syZM4UaU1ZW1pMrFYCHhwfKli1bqOd4EqVSCR8fH8hktvvFSUTFhzHt/nBfzpUHv389h3jCta2rHaMqXMUiqcrOzkavXr0ghMDixYvtHY7Vxo8fD51OZ3pcu3bN3iEVuYdvQLhy5QpkMhmio6PRpk0buLm54dlnnzXr7fv9998REhICV1dXBAQEYNSoUUhLe/BXzqpVq9C4cWN4enrCx8cHffv2xa1bt0zH9+zZA5lMhm3btqFRo0ZQqVT4/fff84xRq9XCx8cH1atXx7Rp05CTk4Pdu3ebjl+7dg29evWCVqtFmTJl0LVrV1y5csV0PCcnB6NGjYJWq0XZsmUxduxYDBgwQNKD17p1a4wYMQKRkZEoV64cwsLCAABnz55Fhw4d4OHhAW9vb7z++uu4ffu26XUbNmxA3bp14erqirJlyyI0NNR0Lfbs2YPnn38e7u7u0Gq1aNGiBa5evQoAmDJlCurXr29qx2g0YurUqfD394dKpUL9+vURExNjOm7tzyY/cn8OycnJAIAVK1ZAq9Vi+/btCA4OhoeHB9q3b4+bN6VryCxbtgzBwcFwcXFBzZo1sWjRoqeOgYjsI+tsFnSfPnR3nwLQfqCFk0/JXsnJ4ZOq3ITq6tWr2LFjh2R3aB8fH8mXKXD/C+7u3bvw8fEx1UlMTJTUyX3+pDoPH3/4dZbqWKJSqaBWqyUPawkhILLs9BDmS/7b0oQJEzBmzBicPHkS1atXR58+fUw9SZcvX0b79u3Ro0cPnD59GmvXrsXvv/+OESNGmF6fnZ2NadOm4dSpU9i4cSOuXLmCgQMHmp1n3LhxmDVrFmJjY1GvXr0nxpWTk4Ovv/4aAEzDxdnZ2QgLC4Onpyf279+PAwcOmJKB3N6m2bNnY/Xq1Vi+fDkOHDgAvV4vuZM118qVK6FUKnHgwAEsWbIEycnJePHFF9GgQQMcO3YMMTExSExMRK9evQDcHzrt06cP3njjDcTGxmLPnj3o3r27qectPDwcrVq1wunTp3Ho0CEMHTo0z16hqKgozJs3D5988glOnz6NsLAwvPzyy2Z3sD7uZ2ML6enp+OSTT7Bq1Srs27cP8fHxGDNmjOn46tWrMWnSJMyYMQOxsbH4+OOPMXHiRKxcudJmMRBR4RFCQP+NHmk/PTTc18oFXh94QaYoBb3WNp3JVQCwMKE5KytLhIeHi9q1a4tbt26ZvSZ3ovqxY8dMZdu3b7c4UT0rK8tUZ/z48WYT1Tt37ixpu1mzZmYT1T/55BPTcZ1OV6gT1Y2ZRrNJfUX1MGZaP3lwwIABomvXrnkef/jnGhcXJwCIZcuWmY6fO3dOABCxsbFCCCEGDx4shg4dKmlj//79Qi6X5zkZ+ejRowKASElJEUI8mCC9cePGJ8YPQLi4uAh3d3chl8sFABEUFCTu3LkjhBBi1apVokaNGsJofHBNMjMzhaurq9i+fbsQQghvb2/JTQw5OTkiMDBQcl1atWolGjRoIDn3tGnTRLt27SRl165dEwDEhQsXxPHjxwUAceXKFbO479y5IwCIPXv2WHxfj05U9/PzEzNmzJDUee6558T//vc/IYR1PxtLKlWqJD777DOLxx6dqL58+XIBQFy6dMlU54svvhDe3t6m51WqVBFr1qyRtDNt2jTRrFkzi+fgRHUix2HQG8y+T3ISbTth3F6KxUT11NRUnDx5EidPngRwf0L4yZMnER8fj+zsbPTs2RPHjh3D6tWrYTAYkJCQgISEBFMPQXBwMNq3b48hQ4bgyJEjOHDgAEaMGIHevXvDz88PANC3b18olUoMHjwY586dw9q1axEVFYV3333XFEdERARiYmIwb948/PXXX5gyZQqOHTtm6h2RyWSIjIzE9OnTsWnTJpw5cwb9+/eHn5/fYydpk2UP9xr5+t5f6Ta3x/HUqVNYsWIFPDw8TI+wsDAYjUbExcUBAI4fP44uXbogMDAQnp6eaNWqFQAgPj5ecp6Hb2B4nM8++wwnT57Etm3bUKtWLSxbtgxlypQxxXPp0iV4enqa4ilTpgwyMjJw+fJl6HQ6JCYm4vnnnze1p1Ao0KhRI7PzPFp26tQp7N69W/Jea9asCeB+j92zzz6Ltm3bom7dunjllVfw1VdfmW6uKFOmDAYOHIiwsDB06dIFUVFRZsNoufR6Pf7991+0aNFCUt6iRQvExsZKyh73s7EFNzc3VKlSRXKO3PbT0tJw+fJlDB48WHJNpk+fjsuXL9ssBiKyvcxTmdDNfzDcJ3ORQTtBC0UF+0wYtxe7Dm4eO3YMbdq0MT3PTXQGDBiAKVOmYNOmTQAgmRsCALt370br1q0B3B8uGDFiBNq2bQu5XI4ePXpgwYIFproajQa//vorhg8fjkaNGqFcuXKYNGmSZC2r5s2bY82aNfjwww/xwQcfoFq1ati4cSPq1KljqvP+++8jLS0NQ4cORXJyMl544QXExMTAxcXF1pflPuf7m0naRd77V9qmeecHJ8gdrjIajQDuJ9pvvfUWRo0aZfa6wMBApKWlISwsDGFhYVi9ejXKly+P+Ph4hIWFmU3+dnd/8t5RwP3h3apVq6Jq1apYvnw5OnbsiPPnz6NChQpITU1Fo0aNsHr1arPXlS9f3ur3bCme1NRUdOnSBbNnzzar6+vrC4VCgR07duDgwYP49ddf8fnnn2PChAk4fPgwKleujOXLl2PUqFGIiYnB2rVr8eGHH2LHjh1o2rRpvuJ62ON+NrbwcPu55xD/P9ycu2jvV199hSZNmkjqKRSl6xczUXEhhEDK0hQYbhlMZa4vusKlRSF9Nzo4uyZVrVu3fuz8nccdy1WmTBmsWbPmsXXq1auH/fsfv0v1K6+8gldeeSXP4zKZDFOnTsXUqVOfGJMtyGQyQPnkeiVNw4YNcf78eVStWtXi8TNnzuDOnTuYNWuW6W7KY8eO2ez8zz//PBo1aoQZM2YgKioKDRs2xNq1a1GhQoU858R5e3vj6NGjaNmyJQDAYDDgxIkTZn8MPKphw4b48ccfERQUBCcny/8VZTIZWrRogRYtWmDSpEmoVKkSfvrpJ9MfIA0aNECDBg0wfvx4NGvWDGvWrDFLqtRqNfz8/HDgwAFTrx4AHDhwQNLDZm/e3t7w8/PDP//8g379+tk7HCJ6AqPOCN0C6VYz6v+poShbev8IKtnT8KlI6HQ60xBurrJlyz7VEhJjx45F06ZNMWLECLz55ptwd3fH+fPnsWPHDixcuBCBgYFQKpX4/PPP8fbbb+Ps2bOYNm2ajd7JfZGRkejWrRvef/999OvXD3PnzkXXrl1Nd89dvXoV0dHReP/99+Hv74+RI0di5syZqFq1KmrWrInPP/8cSUlJT1xKYPjw4fjqq6/Qp08fvP/++yhTpgwuXbqEH374AcuWLcOxY8ewc+dOtGvXDhUqVMDhw4fx33//ITg4GHFxcVi6dClefvll+Pn54cKFC7h48SL69+9v8VzvvfceJk+ejCpVqqB+/fpYvnw5Tp48abEHLr9u3Lhh9vOvVKnSU7X10UcfYdSoUdBoNGjfvj0yMzNx7NgxJCUlSYbsici+Mk9kIn1ruum5zEMGTYQGMnkpmIz+GEyqqMD27NmDBg0aSMoGDx6MZcuW5butevXqYe/evZgwYQJCQkIghECVKlXw6quvArg/5LZixQp88MEHWLBgARo2bIhPPvnEbPuigmjfvj0qV66MGTNmYNGiRdi3bx/Gjh2L7t27IyUlBRUrVkTbtm1NPVdjx45FQkIC+vfvD4VCgaFDhyIsLOyJQ1a5vUdjx45Fu3btkJmZiUqVKqF9+/aQy+VQq9XYt28f5s+fD71ej0qVKmHevHno0KEDEhMT8ddff2HlypW4c+cOfH19MXz4cLz11lsWzzVq1CjodDqMHj0at27dQq1atbBp0yZUq1atwNfrk08+wSeffCIpW7VqFfz9/fPd1ptvvgk3NzfMnTsX7733Htzd3VG3bl1ERkYWOE4iKjghBPSL9DDefTAtwLWdK1yalM7hvkfJhDVjbGQTer0eGo0GOp3ObCgpIyMDcXFxqFy5cuHN06IiYTQaERwcjF69etm8F42k+P+GqOgYkgzQL5TuDKIeoYbCq+QP9z3u+/th7KkiKqCrV6/i119/RatWrZCZmYmFCxciLi4Offv2tXdoREQ2kXEkA/e23zM9l5eRQ/0/NXdMeASTKqICksvlWLFiBcaMGQMhBOrUqYPffvsNwcHB9g6NiKhAhBDQRekgUh4Marl1dIOqkX23H3NUTKqICiggIAAHDhywdxhERDZluGOAfpF0uE8zSgO5xuE3Y7EbJlVEREQkkXEwA/d2PhjuU1RQwHOoJ4f7noBJlYPhfQNE1uP/FyLbEkYB3TwdRMZDw30vu0H1LIf7rMGkykHkrjSdnp4OV1dXO0dDVDzkrqLPFdeJCs7wnwH6JY8M90VqIPfkcJ+1mFQ5CIVCAa1Wa9oHzc3Njd2sRI9hNBrx33//wc3NLc8V6YnIOvf230PGngzTc0VFBTwHcbgvv/ibyIH4+PgAsO0GtkQlmVwuR2BgIH/xEz0lYRBInp0MPNi6D+7d3KGsUwr3SbMBJlUORCaTwdfXFxUqVEB2dra9wyFyeEqlEnI5hyaInkZOYg5SlqZIyjTvaiB35/+pp8WkygEpFArOESEisgOD0YD98ftxM+UmfD19ERIYAoW85P0+vrfrHjIOPBjucwpygufrnnaMqGRgUkVERAQgOjYaETERuK6/birzV/sjqn0Uugd3t2NktiMMAskfJ0vK3Hu6QxnM4T5bYB8fERGVetGx0ei5rqckoQKAG/ob6LmuJ6Jjo+0Ume3k/JtjllBpRmuYUNkQkyoiIirVDEYDImIiIGC+7lluWWRMJAxGg9nx4iJ9RzpSvn4wf8q5mjO8JnpB7sY0wJZ4NYmIqFTbH7/frIfqYQIC1/TXsD9+fxFGZRsiRyBpWhIy/8g0lbm/6g6P3h52jKrk4pwqIiIq1W6m3LRpPUeRcz0HKcsfubvvPQ3kLuxPKSxMqoiIqFTz9fS1aT1HkP5LOjKPP+idcg52hkdP9k4VNiZVRERUqoUEhsBf7Y8b+hsW51XJIIO/2h8hgSF2iC5/RLZA8qxkSZlHXw84V3G2T0ClDPsAiYioVFPIFYhqHwXgfgL1sNzn89vPd/j1qrKvZpslVNr3tUyoihCTKiIiKvW6B3fHhl4bUFFdUVLur/bHhl4bHH6dqrRNaUj9NtX0XFlXCa+JXpCpuIVTUZIJIcz7OqlQ6PV6aDQa6HQ6qNVqe4dDRESPKG4rqous/9+77yEer3vAOYi9U7Zk7fc351QRERH9P4VcgdZBre0dhlWy/8lG6upUSZl2rBYyJXun7IVJFRERUTGT+mMqss9nm54rGyrh3sndjhERwKSKiIio2BAZAslzkyVlngM94RTAr3NHwJ8CERFRMXBv9z1k/J4hKdOO00LmzOE+R8GkioiIyMElTUuSPFc9r4JbmJudoqG8MKkiIiJyUIa7Bui/0EvKPF7zgHNl3t3niJhUEREROaC0n9OQdTpLUqZ9X8u1pxwYkyoiIiIH8+hwHwB4TfSyQySUH0yqiIiIHIThPwP0S6TDfW4vu0H1rMpOEVF+MKkiIiJyAKnrU5H9V7akjHf3FS9MqoiIiOxICIHk6clm5RzuK36YVBEREdlJzs0cpCxLkZS5d3eHsrbSThFRQTCpIiIisoOU1SnI+SdHUqYdr4XMicN9xRWTKiIioiJkabhPppJB+77WLvGQ7TCpIiIiKiI513OQsvyR4b5X3aGszuG+koBJFRERURHQf62H4V+DpEz7gRYyBYf7SgomVURERIXI0nCfXCOHZpTGPgFRoWFSRUREVEiyr2QjdVWqpMyjnwecn+HefSURkyoiIqJCoFukg/GOUVKmnaCFTM7hvpKKSRUREZENCaNA8oxkSZmiggLqt9T2CYiKDJMqIiIiG8m+lI3U76XDfZ4DPOEUWLCvW4PRgP3x+3Ez5SZ8PX0REhgChVxRoDbJ9phUERER2UDyZ8kQqUJSpv1QC5msYMN90bHRiIiJwHX9dVOZv9ofUe2j0D24e4HaJtuS2zsAIiKi4kwYBJKmJUkSKqcAJ3hN9LJJQtVzXU9JQgUAN/Q30HNdT0THRheofbItJlVERERPKeuvLCR/nCwp8xzsCc+BngVu22A0ICImAgLC7FhuWWRMJAxGg9lxsg8mVURERE8haWYS0tanScq0H2rh5GebmTX74/eb9VA9TEDgmv4a9sfvt8n5qOA4p4qIiCgfRI5A8sxkSZlTVSd49il479TDbqbctGk9KnxMqoiIiKyUeSYT6RvTJWWeQz3h5G37r1NfT1+b1qPCx6SKiIjICknTkszKvCZ6Fdr5QgJD4K/2xw39DYvzqmSQwV/tj5DAkEKLgfKHc6qIiIgeQ2QJs4TKubZzoSZUAKCQKxDVPgrA/QTqYbnP57efz/WqHAiTKiIiojxknshE8uxkSZl6mBoe3T2K5Pzdg7tjQ68NqKiuKCn3V/tjQ68NXKfKwciEEOZ9ilQo9Ho9NBoNdDod1GpuV0BE5MiKerjvcbiiun1Z+/3NOVVEREQPERkCyXOTJWXKBkq4d3a3T0C4PxTYOqi13c5P1mFSRURE9P9So1ORfS5bUqYeoYbCi71C9GRMqoiIiOBYw31UPDGpIiKiUs2oN0IXpZOUycvJoRmmsVNEVFwxqSIiolIr5fsU5FzKkZSp31ZDUZ7DfZR/TKqIiKhU4nAf2RqTKiIiKlUMdw3Qf6GXlDkFOMFzoG337qPSh0kVERGVGvpv9DDcMEjKeHcf2QqTKiIiKhU43EeFjUkVERGVaIZbBui/lA73OVd3hserRbPVDJUeTKqIiKjE0n2hg/GuUVKmidBArubWt2R7dv1U7du3D126dIGfnx9kMhk2btwoOS6EwKRJk+Dr6wtXV1eEhobi4sWLkjp3795Fv379oFarodVqMXjwYKSmpkrqnD59GiEhIXBxcUFAQADmzJljFsv69etRs2ZNuLi4oG7duvjll1/yHQsRETmOpGlJZgmV10QvJlRUaOz6yUpLS8Ozzz6LL774wuLxOXPmYMGCBViyZAkOHz4Md3d3hIWFISMjw1SnX79+OHfuHHbs2IEtW7Zg3759GDp0qOm4Xq9Hu3btUKlSJRw/fhxz587FlClTsHTpUlOdgwcPok+fPhg8eDD+/PNPhIeHIzw8HGfPns1XLEREZH85/+aYzZ9S1lNy/hQVPuEgAIiffvrJ9NxoNAofHx8xd+5cU1lycrJQqVTi+++/F0IIcf78eQFAHD161FRn27ZtQiaTiRs3bgghhFi0aJHw8vISmZmZpjpjx44VNWrUMD3v1auX6NSpkySeJk2aiLfeesvqWKyh0+kEAKHT6ax+DRERWS9pbpK4O/Wu5GFINdg7LCrmrP3+dtg+0Li4OCQkJCA0NNRUptFo0KRJExw6dAgAcOjQIWi1WjRu3NhUJzQ0FHK5HIcPHzbVadmyJZRKpalOWFgYLly4gKSkJFOdh8+TWyf3PNbEYklmZib0er3kQUREhSNpWhLEPSEp85roBbm7w37VUQnjsJ+0hIQEAIC3t7ek3Nvb23QsISEBFSpUkBx3cnJCmTJlJHUstfHwOfKq8/DxJ8ViycyZM6HRaEyPgICAJ7xrIiLKr+z4bLPhPtVzKg73UZFz2KSqJBg/fjx0Op3pce3aNXuHRERUoiRNS0LqSunNSZr3NHBr72aniKg0c9ikysfHBwCQmJgoKU9MTDQd8/Hxwa1btyTHc3JycPfuXUkdS208fI686jx8/EmxWKJSqaBWqyUPIqKSwmA0YM+VPfj+zPfYc2UPDEbDk19kI0KIPBfzlLs47FcblXAO+8mrXLkyfHx8sHPnTlOZXq/H4cOH0axZMwBAs2bNkJycjOPHj5vq7Nq1C0ajEU2aNDHV2bdvH7Kzs011duzYgRo1asDLy8tU5+Hz5NbJPY81sRARlSbRsdEIigpCm5Vt0De6L9qsbIOgqCBEx0YX+rmzL2cjeXqypMzlBRcO95Hd2TWpSk1NxcmTJ3Hy5EkA9yeEnzx5EvHx8ZDJZIiMjMT06dOxadMmnDlzBv3794efnx/Cw8MBAMHBwWjfvj2GDBmCI0eO4MCBAxgxYgR69+4NPz8/AEDfvn2hVCoxePBgnDt3DmvXrkVUVBTeffddUxwRERGIiYnBvHnz8Ndff2HKlCk4duwYRowYAQBWxUJEVFpEx0aj57qeuK6/Lim/ob+Bnut6FmpilTQtCalrpMN92rFauLZxLbRzElmtaG5GtGz37t0CgNljwIABQoj7SxlMnDhReHt7C5VKJdq2bSsuXLggaePOnTuiT58+wsPDQ6jVajFo0CCRkpIiqXPq1CnxwgsvCJVKJSpWrChmzZplFsu6detE9erVhVKpFLVr1xZbt26VHLcmlifhkgpEVNzlGHKE/6f+AlNg8SGbIhMBnwaIHEOOTc9rNBrNlkq4O/WuTc9BlBdrv79lQgjxmJyLbEiv10Oj0UCn03F+FREVS3uu7EGblW2eWG/3gN1oHdTaJufMis1C2oY0SZlrW1e4NHexSftET2Lt9zf3/iMiIqvdTLlp03pPYmkyuna8FjInmU3aJ7IlJlVERGQ1X09fm9bLizAKJM9INivnZHRyZEyqiIjIaiGBIfBX++OG/gYEzGePyCCDv9ofIYEhT32OzNOZSP85XVLm1sENqsaqp26TqCg47JIKRETkeBRyBaLaRwG4n0A9LPf5/PbzoZArnqr9pGlJZgmV9gMtEyoqFphUERFRvnQP7o4NvTagorqipNxf7Y8NvTage3D3fLcpDHkv5ilTcP4UFQ+8+68I8e4/IipJDEYD9sfvx82Um/D19EVIYMhT9VBlHMvAvW33JGVuXd2gqsfeKXIMvPuPiIgKlUKuKPCyCRbv7vtQC5mMvVNU/DCpIiKiIidyBJJnJpuV8+4+Ks6YVBERUZHKOJCBe7ukw33ur7hDWVNpp4iIbINJFRERFRkO91FJxqSKiIgKncgSSJ6dbFbO4T4qSZhUERFRobq36x4yDmRIyjz6esC5irOdIiIqHEyqiIiKgK2WHyhuONxHpQmTKiKiQhYdG42ImAhc1183lfmr/RHVPuqpFsosDowZRujm6szKOdxHJVm+kiqj0Yi9e/di//79uHr1KtLT01G+fHk0aNAAoaGhCAgIKKw4iYiKpejYaPRc19Nsn7wb+hvoua7nU69A7sjSY9KReTRTUuY5wBNOgfw7nko2q7apuXfvHqZPn46AgAB07NgR27ZtQ3JyMhQKBS5duoTJkyejcuXK6NixI/7444/CjpmIqFgwGA2IiImwuPFwbllkTCQMRkNRh1ZokqYlmSVUXhO9mFBRqWDVp7x69epo1qwZvvrqK7z00ktwdjafXHj16lWsWbMGvXv3xoQJEzBkyBCbB0tEVJzsj98vGfJ7lIDANf017I/fX+CVye3NmGaE7lPpcJ/MTQbtaK19AiKyA6uSql9//RXBwcGPrVOpUiWMHz8eY8aMQXx8vE2CIyIqzm6m3LRpPUeVtjENWWeyJGWeb3rCyZe9U1S6WPWJf1JC9TBnZ2dUqVLlqQMiIiopfD19bVrPEVm6u4+T0am0smpOlTWio6NRr149WzVHRFTshQSGwF/tDxksLx8ggwwB6gCEBIYUcWQFZ7hjMEuo5GXlTKioVMtXUvXll1+iZ8+e6Nu3Lw4fPgwA2LVrFxo0aIDXX38dLVq0KJQgiYiKI4Vcgaj2UQBglljlPp/ffr7d16syGA3Yc2UPvj/zPfZc2fPEifPJc5OhX6SXlKnfUkPzP01hhknk8GRCCPPbUiyYNWsWJk2ahHr16uGvv/6CEAITJkzA559/joiICLz11lvw8uJfKI+j1+uh0Wig0+mgVqvtHQ4RFRFL61QFqAMwv/18uy+nkN81tDjcR6WRtd/fVidVNWrUwAcffIABAwZg//79aNWqFTp27Ii1a9fC3d3dZoGXZEyqiEovR1xRPa81tHJ70R5eQysnMQcpS1PM2mBCRaWBzZMqV1dX/P3336YFPlUqFQ4ePIhGjRrZJuJSgEkVETkKg9GAoKigPJd8kEEGf7U/4iLioJ+hNzvuOdQTTt68u49KB2u/v63+H5GZmQkXFxfTc6VSiTJlyhQsSiIisgtr19CylFCxd4rIsnz9mTFx4kS4ubkBALKysjB9+nRoNNKJiZ9++qntoiMiokLxpLWxGqMxfpX/Ki1UAV7vM6EiyovVSVXLli1x4cIF0/PmzZvjn3/+kdThruNERMXD49bGuiu/a1am/p8airL2nQNG5OisTqr27NlTiGEQEVFRyl1D64b+hmSiuqWEisN9RNax2eKfRERUfDy6hlZLtDRLqNI90plQEeWD1T1V3btbXktFo9GgevXqePPNN1G+fHmbBUZERIWre3B3bOi1AW02tDE7drDjQXRq1MkOUREVX1b3VGk0GouP5ORkfPXVV6hRowbOnj1bmLESEZGNWUqo1BPUTKiInoLV61Q9jtFoxJAhQ3Dr1i1s3rzZFnGVSFyniogcRdaFLKStS5OUKSoqoH6Dv5uIHmXzdaoeRy6XY9SoUejQoYMtmiMiokJkaasZzbsayN05zZaoIGy2HK67uzvS09Nt1RwREdmYEALJ05PNyjkZncg2bJZU7dixA9WrV7dVc0REZEOZpzOR/rP0D1/nas7w6O1hp4iISh6rk6pNmzZZLNfpdDh+/DiWLVuGZcuW2SwwIiKyDUvDfdr3tJC5cMFmIluyOqkKDw+3WO7p6YkaNWpg2bJl6N27t63iIiKiAuJwH1HRsjqpMhqNhRkHERHZUMaxDNzbdk9SpqynhHtXdztFRFTy2WxOFREROQaLw31jtZApOdxHVJisTqq+/fZbq+r179//qYMhIqKnJ4wCyTOSzco53EdUNKxe/FMul8PDwwNOTk7I6yUymQx375pvxkn3cfFPIios936/h4zdGZIyVRMV3Nq52SkiopLD5ot/BgcHIzExEa+99hreeOMN1KtXzyaBEhFRwVgc7huvhcyJw31ERcnq5XPPnTuHrVu34t69e2jZsiUaN26MxYsXQ6/XF2Z8RESUB2EQFhMqr4leTKiI7OCp9v67d+8e1q9fj+XLl+PIkSMIDw/HN998A5VKVRgxlhgc/iMiW0n/LR2ZhzIlZS6tXODa0tVOERGVXNZ+fxdoQ+V9+/Zh8uTJ2LdvH27fvg0vL06GfBwmVURkCxaH+yZoIZOzd4qoMFj7/Z3v3TNv3LiBjz/+GNWqVUPv3r3x3HPP4dy5c0yoiIgKmch+zHAfEyoiu7N6ovq6deuwfPly7N27F2FhYZg3bx46deoEhUJRmPERERGAtC1pyPozS1LmGuYKl+dd7BQRET0qX0sqBAYGol+/fvD29s6z3qhRo2wWXEnD4T8iehoWh/s+1EImY+8UUVGw+ZyqoKCgJ/4Hlslk+Oeff/IXaSnCpIqI8kNkCCTPTTYr52KeREXL5utUXblyxRZxERGRFVLXpSL7QrakzO1lN6ie5V3WRI6Ke/8RETkYDvcRFU9MqoiIHIQx3QjdPJ1ZOYf7iIoHJlVERA4gZWUKcuJzJGXuPd2hDFbaKSIiyi8mVUREdpbX2lNEVLwwqSIishOj3ghdFIf7iEqKp0qqjEYjLl26hFu3bsFoNEqOtWzZ0iaBERGVZLrFOhhvS39/evT1gHMVZztFREQFle+k6o8//kDfvn1x9epVPLrElUwmg8FgsFlwREQlEYf7iEqmfCdVb7/9Nho3boytW7fC19eXt/gSEVnJcNsA/WK9WTkTKqKSId9J1cWLF7FhwwZUrVq1MOIhIiqRLPVOeQ70hFMAp7YSlRTy/L6gSZMmuHTpUmHEQkRUIuU13MeEiqhkyff/6JEjR2L06NFISEhA3bp14ewsnVRZr149mwVHRFRcGYwGHD9+HNViqpkd43AfUclk9YbKueRy884tmUwGIQQnqj8BN1QmKh2iY6PRZkMbs/ITrU+gbUhbO0RERAVh8w2Vc8XFxRUoMCKikiyvhKqssSywC9hQbgO6B3e3Q2REVNjy3VNFT489VUQlW+blTKSvSTcrL2MsAwCQQQZ/tT/iIuKgkCuKOjwieko27anatGkTOnToAGdnZ2zatOmxdV9++eX8RUpEVAJYmoze0tgSZ3HW9FxA4Jr+GvbH70froNZFGB0RFQWrkqrw8HAkJCSgQoUKCA8Pz7Me51QRUWlkKaHK7Z2y5GbKzcIMh4jsxKolFYxGIypUqGD6d14PWydUBoMBEydOROXKleHq6ooqVapg2rRpkpXchRCYNGkSfH194erqitDQUFy8eFHSzt27d9GvXz+o1WpotVoMHjwYqampkjqnT59GSEgIXFxcEBAQgDlz5pjFs379etSsWRMuLi6oW7cufvnlF5u+XyIqXrL+ysp3QgUAvp6+hRUSEdlRvtepKkqzZ8/G4sWLsXDhQsTGxmL27NmYM2cOPv/8c1OdOXPmYMGCBViyZAkOHz4Md3d3hIWFISMjw1SnX79+OHfuHHbs2IEtW7Zg3759GDp0qOm4Xq9Hu3btUKlSJRw/fhxz587FlClTsHTpUlOdgwcPok+fPhg8eDD+/PNPhIeHIzw8HGfPPujaJ6LSI2laEtLWp0nK3Ie5o55HPchgeacJGWQIUAcgJDCkKEIkoiJm1UT1H374Ab1797aqwWvXriE+Ph4tWrQocHCdO3eGt7c3vv76a1NZjx494Orqiu+++w5CCPj5+WH06NEYM2YMAECn08Hb2xsrVqxA7969ERsbi1q1auHo0aNo3LgxACAmJgYdO3bE9evX4efnh8WLF2PChAlISEiAUqkEAIwbNw4bN27EX3/9BQB49dVXkZaWhi1btphiadq0KerXr48lS5ZY9X44UZ2oZHjc3n3RsdHoua4ngPtzqHLlJlobevHuP6Lixtrvb6t6qhYvXozg4GDMmTMHsbGxZsd1Oh1++eUX9O3bFw0bNsSdO3eePvKHNG/eHDt37sTff/8NADh16hR+//13dOjQAcD95R0SEhIQGhpqeo1Go0GTJk1w6NAhAMChQ4eg1WpNCRUAhIaGQi6X4/Dhw6Y6LVu2NCVUABAWFoYLFy4gKSnJVOfh8+TWyT2PJZmZmdDr9ZIHERVfmacyzRMquXQxz+7B3bGh1wZUVFeUVPNX+zOhIirhrJqovnfvXmzatAmff/45xo8fD3d3d3h7e8PFxQVJSUlISEhAuXLlMHDgQJw9exbe3t42CW7cuHHQ6/WoWbMmFAoFDAYDZsyYgX79+gEAEhISAMDsfN7e3qZjuRPsH+bk5IQyZcpI6lSuXNmsjdxjXl5eSEhIeOx5LJk5cyY++uij/L5tInJAlnqnNBEayNXmf5t2D+6OrjW6Yn/8ftxMuQlfT1+EBIZwGQWiEs7qxT9ffvllvPzyy7h9+zZ+//13XL16Fffu3UO5cuXQoEEDNGjQwOJq6wWxbt06rF69GmvWrEHt2rVx8uRJREZGws/PDwMGDLDpuQrD+PHj8e6775qe6/V6BAQE2DEiInoajxvuy4tCruCyCUSlTL5XVC9Xrtxjl1Wwpffeew/jxo0zzeeqW7curl69ipkzZ2LAgAHw8fEBACQmJsLX98HdNImJiahfvz4AwMfHB7du3ZK0m5OTg7t375pe7+Pjg8TEREmd3OdPqpN73BKVSgWVSpXft01EDiLjcAbu/XpPUiZTy6CN0NonICJyaA599196erpZ75dCoYDRaAQAVK5cGT4+Pti5c6fpuF6vx+HDh9GsWTMAQLNmzZCcnIzjx4+b6uzatQtGoxFNmjQx1dm3bx+ys7NNdXbs2IEaNWrAy8vLVOfh8+TWyT0PEZUsSdOSzBIqzbsaJlRElCeHTqq6dOmCGTNmYOvWrbhy5Qp++uknfPrpp+jWrRuA+4uNRkZGYvr06di0aRPOnDmD/v37w8/Pz9SbFhwcjPbt22PIkCE4cuQIDhw4gBEjRqB3797w8/MDAPTt2xdKpRKDBw/GuXPnsHbtWkRFRUmG7iIiIhATE4N58+bhr7/+wpQpU3Ds2DGMGDGiyK8LERUeIUSew31yd4f+lUlE9iYcmF6vFxERESIwMFC4uLiIZ555RkyYMEFkZmaa6hiNRjFx4kTh7e0tVCqVaNu2rbhw4YKknTt37og+ffoIDw8PoVarxaBBg0RKSoqkzqlTp8QLL7wgVCqVqFixopg1a5ZZPOvWrRPVq1cXSqVS1K5dW2zdujVf70en0wkAQqfT5et1RFQ00neni7tT70oeuq/4/5WotLP2+5sbKhchrlNF5Lgs9U5p39dCprK8kCcRlR42XafqYbt37y5QYEREjkQY8x7uY0JFRPmR76Sqffv2qFKlCqZPn45r164VRkxEREUifVs6kmckS8qcqjo9cbkEIiJL8p1U3bhxAyNGjMCGDRvwzDPPICwsDOvWrUNWVlZhxEdEVCiSpiUh81impEw7XgvPPp52ioiIirt8J1XlypXDO++8g5MnT+Lw4cOoXr06/ve//8HPzw+jRo3CqVOnCiNOIiKbEIbHDPc5cbiPiJ5egSeq//vvv1i6dClmzZoFJycnZGRkoFmzZliyZAlq165tqzhLBE5UJ7Kv1B9TkX0+W1KmrKeEe1d3O0VERMVBoU1UB4Ds7Gxs2LABHTt2RKVKlbB9+3YsXLgQiYmJuHTpEipVqoRXXnnlqYMnIrK1pGlJZgmV9gMtEyoispl891SNHDkS33//PYQQeP311/Hmm2+iTp06kjoJCQnw8/MzrXxO97GniqjoiSyB5NnJZuWcjE5E1rL2+zvfe/+dP38en3/+Obp3757nvnblypXj0gtEZHcpq1KQcyVHUqZqqoLbS252ioiISrJ8JVXZ2dmoVKkSmjZt+tiNgp2cnNCqVasCB0dE9LQsLub5oRYyGSejE1HhyNecKmdnZ/z444+FFQsRUYEZ7xnzvruPCRURFaJ8T1QPDw/Hxo0bCyEUIqKC0S3WQfeJTlLm0saF86eIqEjke05VtWrVMHXqVBw4cACNGjWCu7v0zplRo0bZLDgiImtxuI+I7C3fd/9Vrlw578ZkMvzzzz8FDqqk4t1/RLZnTDVC95nOrJy9U0RkK4V2919cXFyBAiMishVLvVNuHdygapz3jTRERIUl30kVEZEjyGsyOhGRvTxVUnX9+nVs2rQJ8fHxZhspf/rppzYJjIjIEsMdA/SL9GblTKiIyN7ynVTt3LkTL7/8Mp555hn89ddfqFOnDq5cuQIhBBo2bFgYMRIRAbDcO+XawRUujV3sEA0RkVS+l1QYP348xowZgzNnzsDFxQU//vgjrl27hlatWnG/PyIqNHkN9zGhIiJHke+kKjY2Fv379wdwf+X0e/fuwcPDA1OnTsXs2bNtHiARlW45N3M4f4qIioV8D/+5u7ub5lH5+vri8uXLqF27NgDg9u3bto2OiEo1S8mUezd3KOso7RANEdHj5Tupatq0KX7//XcEBwejY8eOGD16NM6cOYPo6Gg0bdq0MGIkolKIvVNEVNzkO6n69NNPkZqaCgD46KOPkJqairVr16JatWq884+ICiz7ajZSv001K2dCRUSOLt8rqtPT44rqRI9nqXfKo48HnKs62yEaIqL7Cm1FdSKiwsDhPiIq7qxKqry8vKzelPTu3bsFCoiISpesC1lIW5dmVs6EioiKG6uSqvnz5xdyGERUGlnqnfIc6AmnAHaiE1HxY9VvrgEDBhR2HERUynC4j4hKmgL9OZiRkWG29x8nYBPR42SeykT6pnSzciZURFTc5TupSktLw9ixY7Fu3TrcuXPH7LjBYLBJYERU8lgc7hvqCSdvDvcRUfGX721q3n//fezatQuLFy+GSqXCsmXL8NFHH8HPzw/ffvttYcRIRCVAXsN9TKiIqKTI92+zzZs349tvv0Xr1q0xaNAghISEoGrVqqhUqRJWr16Nfv36FUacRFRMZRzOwL1f75mVc7iPiEqafCdVd+/exTPPPAPg/vyp3CUUXnjhBQwbNsy20RFRsWapd0o9XA1FGYUdoiEiKlz5Hv575plnEBcXBwCoWbMm1q1bB+B+D5ZWq7VpcERUfOU13MeEiohKqnz3VA0aNAinTp1Cq1atMG7cOHTp0gULFy5EdnY29/4jItzbcw8Z+zPMyjncR0QlXYH3/rty5QpOnDiBqlWrol69eraKq0Ti3n9U0lnqndJEaiD3zHenOAxGA/bH78fNlJvw9fRFSGAIFHL2chFR0Suyvf+CgoIQFBRU0GaIqBgTQiB5erJZ+dP2TkXHRiMiJgLX9ddNZf5qf0S1j0L34O5PGyYRUaGy+s/HQ4cOYcuWLZKyb7/9FpUrV0aFChUwdOhQZGZm2jxAInJsab+kmSdUioIlVD3X9ZQkVABwQ38DPdf1RHRs9FNGSkRUuKxOqqZOnYpz586Znp85cwaDBw9GaGgoxo0bh82bN2PmzJmFEiQROaakaUnIOi7dVUEzRgOvD54uoTIYDYiIiYCA+ayE3LLImEgYjFxkmIgcj9VJ1cmTJ9G2bVvT8x9++AFNmjTBV199hXfffRcLFiww3QlIRCWbMIo87+6Tu+Z//lSu/fH7zXqoJOeFwDX9NeyP3//U5yAiKixWz6lKSkqCt7e36fnevXvRoUMH0/PnnnsO165ds210RORwUtenIvuvbEmZ3EsOzQhNgdu+mXLTpvWIiIqS1X9Sent7m9anysrKwokTJ9C0aVPT8ZSUFDg7O9s+QiJyGEnTkswSKu1YrU0SKgDw9fS1aT0ioqJkdU9Vx44dMW7cOMyePRsbN26Em5sbQkJCTMdPnz6NKlWqFEqQRGRfIkcgeWayWbmt154KCQyBv9ofN/Q3LM6rkkEGf7U/QgLv/+7hsgtE5EisTqqmTZuG7t27o1WrVvDw8MDKlSuhVCpNx7/55hu0a9euUIIkIvvRL9fDcF06MdwpwAmeAz1tfi6FXIGo9lHoua4nZJBJEisZZACA+e3nQyFXcNkFInI4+V78U6fTwcPDAwqF9K/Bu3fvwsPDQ5JokRQX/6TixtJkdO0HWsgUskI9r6WEKUAdgPnt56N7cHfTsguP9mblJl4bem1gYkVENmPt93eBV1Qn6zGpouJCZAkkz042Ky/KrWbyGtozGA0IigrK8y7B3CHCuIg4DgUSkU0U2YrqRFSy6D7XwZhslJQ5BzvDo6dHkcahkCvQOqi1WXl+ll2w9HoiosLCpIqITCwO903QQiYv3OG+/OCyC0TkqJhUEdlYcbwjzXjPCN0nOrPyohzusxaXXSAiR8WkisiGiuMdaUkzkgDpaB9UjVVw6+Bmn4CeIL/LLhARFZWn30+CiCSK40bASdPMEyrth1qHTaiAB8suAA/u9sv16LILRERFiUkVkQ0Ut42AjXpjnnv3yWSOM38qL92Du2NDrw2oqK4oKfdX+3M5BSKyGw7/EdlAcbojzVIy5RLiAtfWrnaI5ul1D+6OrjW6Frv5a0RUcjGpIrKB4nJHWl69U8VVXssuEBHZA5MqIhtw9DvSDEkG6BfqzcqLc0JFRORomFQR2YAj35FmqXfKNcwVLs+7FHksREQlGSeqE9mAo96RltdwHxMqIiLbY1JFZCOOdEea4ZahxM2fIiJydNxQuQhxQ+XSwd4rqltKptx7uENZS1lkMRARlSTcUJnITux5Rxp7p4iI7IdJFVEJkHM9BynLU8zKmVARERUdJlVExZyl3imPvh5wruJc5LHYe+iTiMiemFQRFWOONNxXHDeTJiKyJd79R1QMZf+T7XAJVXHbTJqIyNbYU0VUzFhKpjwHecLJ3z7/nZ+0mbQMMkTGRKJrja4cCiSiEo09VUT5YDAasOfKHnx/5nvsubIHBqOhSM+fV++UvRIqIH+bSRMRlWQOn1TduHEDr732GsqWLQtXV1fUrVsXx44dMx0XQmDSpEnw9fWFq6srQkNDcfHiRUkbd+/eRb9+/aBWq6HVajF48GCkpqZK6pw+fRohISFwcXFBQEAA5syZYxbL+vXrUbNmTbi4uKBu3br45ZdfCudNk0OKjo1GUFQQ2qxsg77RfdFmZRsERQUVydBWVmyWQw33Pay4bCZNRFTYHDqpSkpKQosWLeDs7Ixt27bh/PnzmDdvHry8HnyRzJkzBwsWLMCSJUtw+PBhuLu7IywsDBkZGaY6/fr1w7lz57Bjxw5s2bIF+/btw9ChQ03H9Xo92rVrh0qVKuH48eOYO3cupkyZgqVLl5rqHDx4EH369MHgwYPx559/Ijw8HOHh4Th79mzRXAyyK3vOGUqaloS0DWmSMvVbaodIqADH30yaiKioOPSK6uPGjcOBAwewf7/lYQMhBPz8/DB69GiMGTMGAKDT6eDt7Y0VK1agd+/eiI2NRa1atXD06FE0btwYABATE4OOHTvi+vXr8PPzw+LFizFhwgQkJCRAqVSazr1x40b89ddfAIBXX30VaWlp2LJli+n8TZs2Rf369bFkyRKr3g9XVC+eDEYDgqKC8hziyt0sOS4izuZzhhy1d+phudfnSZtJF8b1ISIqCtZ+fzt0T9WmTZvQuHFjvPLKK6hQoQIaNGiAr776ynQ8Li4OCQkJCA0NNZVpNBo0adIEhw4dAgAcOnQIWq3WlFABQGhoKORyOQ4fPmyq07JlS1NCBQBhYWG4cOECkpKSTHUePk9undzzWJKZmQm9Xi95UPFjjzlDmX9mFouECnDczaSJiIqaQydV//zzDxYvXoxq1aph+/btGDZsGEaNGoWVK1cCABISEgAA3t7ektd5e3ubjiUkJKBChQqS405OTihTpoykjqU2Hj5HXnVyj1syc+ZMaDQa0yMgICBf758cQ1HPGUqaloT0LemSMvUIxxnus8SRNpMmIrIXh15SwWg0onHjxvj4448BAA0aNMDZs2exZMkSDBgwwM7RPdn48ePx7rvvmp7r9XomVsVQUc4ZKi69U5Z0D+6OrjW6ckV1Iiq1HDqp8vX1Ra1atSRlwcHB+PHHHwEAPj4+AIDExET4+j74QktMTET9+vVNdW7duiVpIycnB3fv3jW93sfHB4mJiZI6uc+fVCf3uCUqlQoqlcqq90qOKyQwBP5q/yfOGQoJDHnqc2QczMC9nffMyotLQpXLnptJExHZm0MP/7Vo0QIXLlyQlP3999+oVKkSAKBy5crw8fHBzp07Tcf1ej0OHz6MZs2aAQCaNWuG5ORkHD9+3FRn165dMBqNaNKkianOvn37kJ2dbaqzY8cO1KhRw3SnYbNmzSTnya2Tex4quQp7zlDStCSzhEoTqSl2CRURUWnn0EnVO++8gz/++AMff/wxLl26hDVr1mDp0qUYPnw4AEAmkyEyMhLTp0/Hpk2bcObMGfTv3x9+fn4IDw8HcL9nq3379hgyZAiOHDmCAwcOYMSIEejduzf8/PwAAH379oVSqcTgwYNx7tw5rF27FlFRUZKhu4iICMTExGDevHn466+/MGXKFBw7dgwjRowo8utCRa8w5gwJIfIc7pN7OvR/TSIiskQ4uM2bN4s6deoIlUolatasKZYuXSo5bjQaxcSJE4W3t7dQqVSibdu24sKFC5I6d+7cEX369BEeHh5CrVaLQYMGiZSUFEmdU6dOiRdeeEGoVCpRsWJFMWvWLLNY1q1bJ6pXry6USqWoXbu22Lp1a77ei06nEwCETqfL1+vIceQYcsTuuN1izek1YnfcbpFjyHmqdtJ/Sxd3p96VPJLmJdk2WCIisglrv78dep2qkobrVBFgeTK6ZowGclfreqcMRgMngxMRFSFrv78deqI6UUkihEDy9GSz8vzMnYqOjUZETIRk3Sx/tT+i2kdZNQTJhIyIqPAwqSLKgy0TkLTNacg6mSUpU/gooB5ifY9l7lY5j96BmLtVzpPmdhU0ISMiosfj8F8R4vBf8WHLBMTScJ92rBYypcxCbcsKulVOXglZ7t2LXKCTiChvJWKbGiJ7sNXmycKQ9919+UmogIJtlWMwGhARE2Fxja3cssiYSBiMhnzFREREUkyqiB5iqwQk9YdUJH+cLClzqur01GtPFWSrHHvsXUhEVBoxqSJ6iC0SkKRpSci+mC0p036ghWcfz6eOqyBb5RT13oVERKUVkyqihxQkARE5jxnuU+RvuO9RuVvlPLqiey4ZZAhQB1jcKqco9y4kIirNmFQRPeRpE5B7e+4heWaypMyto5vNtpopyFY5BUnIiIjIekyqiB7yNAlI0rQkZOzPkNTTfqiFqpFtN9N+2q1yCnvvQiIiuo9LKhQhLqlQPOTe/QdAMmH90eUHRJZA8uxks9cX9kbIT7t+lqVlIgLUAZjffj6XUyAiegxrv7+ZVBUhJlXFx5MSkPTt6cg8kil5jXs3dyjrKB161XJHjo2IyFExqXJATKqKl7wSEIuLeX6ohUwm46rlREQlEJMqB8SkqngTGQLJc5PNynOH+7hqORFRycQNlYlsKG1jGrLOSPfuc3/VHcrqSgBPXjRUBhkiYyLRtUZXDrcREZVQvPuP6AmSpiWZJVReE71MCRXAVcuJiIg9VUR5MqYZoftUZ1Zu6e4+rlpORERMqogsSNuahqwT0t4pj9c84FzZ2WJ9rlpORERMqogekddWM4+Tu2joDf0Ni/OqZJDBX+3PVcuJiEowzqki+n/GVKNZQiXzlFm1mCdXLSciIiZVRABSf0yF7jPp/CnPIZ7QRmqtbuNpt5EhIqKSgetUFSGuU+WYnma473G4ajkRUcnCdaqInsCoM0K3QNo7pfBWQD20YAmvQq5A66DWBWqDiIiKHyZVVCqlrE5Bzj85kjL1MDUU5dijRERET4dJFZU6th7uIyIiAphUUSliuGOAfpFeUuZUyQme/T3tFBEREZUkTKqoVNAv08Nw0yApU49UQ6HlcB8REdkGkyoq8TjcR0RERYFJFZVYhlsG6L+UDvc513CGRy8PO0VEREQlGZMqKpF0C3UwJhklZZpIDeSeXO+WiIgKB5MqKnE43EdERPbApIpKjJwbOUj5JkVSpnxWCfeX3e0UERERlSZMqqhESJ6TDJEp3XFJM1oDuRuH+4iIqGgwqaJij8N9RETkCJhUUbGVfTUbqd+mSspUTVRwa+dmp4iIiKg0Y1JFxZKl3inte1rIXGR2iIaIiIhJFRUzQggkT082K+dwHxER2RuTKio2si9lI/V76XCfS4gLXFu72ikiIiKiB5hUUbFgcbhvnBYyZw73ERGRY2BSRQ6Nw31ERFRcMKkih5V1PgtpP6ZJylxDXeHSzMVOEREREeWNSRU5JIvDfeO1kDlxuI+IiBwTk6pSwmA0YH/8ftxMuQlfT1+EBIZAIVfYOywzHO4jIqLiiklVKRAdG42ImAhc1183lfmr/RHVPgrdg7vbMTKp7CvZSF0lvbvPrZMbVA1VdoqIiIjIetwYrYSLjo1Gz3U9JQkVANzQ30DPdT0RHRttp8ikdIt0ZgmVdoKWCRURERUbTKpKMIPRgIiYCAgIs2O5ZZExkTAYDUUd2oM4jAJJ05JgvGM0lSm8FfCa6AWZnPOniIio+GBSVYLtj99v1kP1MAGBa/pr2B+/vwijeiD7UjaSZyRLyjwHeEI9VG2XeIiIiAqCc6pKsJspN21az5aSP0uGSJX2oGk/1EImY+8UEREVT0yqSjBfT1+b1rMFYRBI/jhZUuYU6ATPAZ5FFgMREVFhYFJVgoUEhsBf7Y8b+hsW51XJIIO/2h8hgSFFEk/WX1lIWy9dzNNzsCec/PgxJCKi4o9zqkowhVyBqPZRAO4nUA/LfT6//fwiWa8qaWaSWUKl/VDLhIqIiEoMJlUlXPfg7tjQawMqqitKyv3V/tjQa0Ohr1Mlcu7f3YecB2XO1Zzv393H+VNERFSCyIQQ5uNCVCj0ej00Gg10Oh3U6qK9w80eK6pnnslE+sZ0SZn7EHcczDjo8Cu7ExER5bL2+5tjL6WEQq5A66DWRXY+S3v37e65GxHfO/7K7kRERE+Dw39kUyJLmCVUzrWdsbvn7mKxsjsREdHTYlJFNpN5IhPJs5MlZephariGuzr8yu5EREQFxaSKbCJpWhLSt0rnT3lN9IKinMLhV3YnIiKyBc6pogIRGQLJc5MlZcoGSrh3djc9d+SV3YmIiGyFSRU9tYzDGbj36z1JmXqEGgov6d18jriyOxERka0xqaKnYunuPq+JXhbrOtrK7kRERIWBc6ooX4zpRrOEStVUlWdCBTjWyu5ERESFhUkVWe3e7/egm6eTlGkiNHB7ye2Jr7X3yu5ERESFjSuqFyF7rqheUPkZ7nsce6zsTkREVBBcUZ1swphqhO4zae+US4gLXFu7PlV7Rb2yOxERUVFhUkV5urfrHjIOZEjKNO9oIPfgqDEREdGjmFSRRbYa7iMiIiotmFSRhFFnhG6BdLjPta0rXJq7FFkMnHdFRETFUbEax5k1axZkMhkiIyNNZRkZGRg+fDjKli0LDw8P9OjRA4mJiZLXxcfHo1OnTnBzc0OFChXw3nvvIScnR1Jnz549aNiwIVQqFapWrYoVK1aYnf+LL75AUFAQXFxc0KRJExw5cqQw3qbdpMekmyVUmjGaIk2oomOjERQVhDYr26BvdF+0WdkGQVFB3HCZiIgcXrFJqo4ePYovv/wS9erVk5S/88472Lx5M9avX4+9e/fi33//RffuD27PNxgM6NSpE7KysnDw4EGsXLkSK1aswKRJk0x14uLi0KlTJ7Rp0wYnT55EZGQk3nzzTWzfvt1UZ+3atXj33XcxefJknDhxAs8++yzCwsJw69atwn/zj2EwGrDnyh58f+Z77Lmy56k3JU6aloTMo5mSMq+JXpC7Ft1HJDo2Gj3X9TTbJ/CG/gZ6ruvJxIqIiBxasVhSITU1FQ0bNsSiRYswffp01K9fH/Pnz4dOp0P58uWxZs0a9OzZEwDw119/ITg4GIcOHULTpk2xbds2dO7cGf/++y+8vb0BAEuWLMHYsWPx33//QalUYuzYsdi6dSvOnj1rOmfv3r2RnJyMmJgYAECTJk3w3HPPYeHChQAAo9GIgIAAjBw5EuPGjbPqfdh6SYXo2GhExERIkhB/tT+i2kdZve6T4a4B+i/0kjLX9q5wea7oeqeA+8lhUFRQnhsv5666HhcRx6FAIiIqUtZ+fxeLnqrhw4ejU6dOCA0NlZQfP34c2dnZkvKaNWsiMDAQhw4dAgAcOnQIdevWNSVUABAWFga9Xo9z586Z6jzadlhYmKmNrKwsHD9+XFJHLpcjNDTUVMeSzMxM6PV6ycNWbNGrk7YpzSyh0r6vLfKECgD2x+/PM6ECAAGBa/pr2B+/vwijIiIisp7DJ1U//PADTpw4gZkzZ5odS0hIgFKphFarlZR7e3sjISHBVOfhhCr3eO6xx9XR6/W4d+8ebt++DYPBYLFObhuWzJw5ExqNxvQICAiw7k0/gcFoQERMhMV99HLLImMiHzsUmDQtCVmnsiRlXhO9IFPJ8nhF4bqZctOm9YiIiIqaQydV165dQ0REBFavXg0Xl6LvPSmo8ePHQ6fTmR7Xrl2zSbsF6dUx/GcwWy7BrYub3ZdL8PX0tWk9IiKioubQSyocP34ct27dQsOGDU1lBoMB+/btw8KFC7F9+3ZkZWUhOTlZ0luVmJgIHx8fAICPj4/ZXXq5dwc+XOfROwYTExOhVqvh6uoKhUIBhUJhsU5uG5aoVCqoVKr8v/EneNpendQNqciOzZaUacdqIVPap3fqYSGBIfBX++OG/obFHrjcOVUhgSF2iI6IiOjJHLqnqm3btjhz5gxOnjxpejRu3Bj9+vUz/dvZ2Rk7d+40vebChQuIj49Hs2bNAADNmjXDmTNnJHfp7dixA2q1GrVq1TLVebiN3Dq5bSiVSjRq1EhSx2g0YufOnaY6RSm/vTpCCOhX6M0SKq+JXg6RUAH3t6+Jah8F4H4C9bDc5/Pbz+ckdSIiclgO3VPl6emJOnXqSMrc3d1RtmxZU/ngwYPx7rvvokyZMlCr1Rg5ciSaNWuGpk2bAgDatWuHWrVq4fXXX8ecOXOQkJCADz/8EMOHDzf1Ir399ttYuHAh3n//fbzxxhvYtWsX1q1bh61bt5rO++6772LAgAFo3Lgxnn/+ecyfPx9paWkYNGhQEV2NB/LTq2Np7z73bu5Q1lEWVbhW6x7cHRt6bbB4R+P89vOtvqORiIjIHhw6qbLGZ599Brlcjh49eiAzMxNhYWFYtGiR6bhCocCWLVswbNgwNGvWDO7u7hgwYACmTp1qqlO5cmVs3boV77zzDqKiouDv749ly5YhLCzMVOfVV1/Ff//9h0mTJiEhIQH169dHTEyM2eT1opDbq9NzXU/IIJMkVg/36uSczUH6z+kPXqgEtO9pIZM7Ru+UJd2Du6Nrja5cUZ2IiIqdYrFOVUlRFOtUBagDMD9sPkIPhMKQ8ODuP5c2LnB9wbXA5yQiIiptrP3+ZlJVhGydVAHm++S10LZA6uepkjrqYWooyrGnh4iI6GlY+/1d7If/SjuFXIHWQa0BAJl/ZiJ11YOESuYugyZS49DDfURERCUFk6oSQAgB/WI9jHeMpjLXl1zh0rT4re1FRERUXDGpKgEyj2ZKEir1cDUUZTjcR0REVJSYVJUACq/7CZRcK4d6hBoyGYf7iIiIihqTqhLAuZqz3beZISIiKu0cekV1IiIiouKCSRURERGRDTCpIiIiIrIBJlVERERENsCkioiIiMgGmFQRERER2QCTKiIiIiIbYFJFREREZANMqoiIiIhsgEkVERERkQ0wqSIiIiKyASZVRERERDbApIqIiIjIBphUEREREdmAk70DKE2EEAAAvV5v50iIiIjIWrnf27nf43lhUlWEUlJSAAABAQF2joSIiIjyKyUlBRqNJs/jMvGktItsxmg04t9//4WnpydkMpm9w7EbvV6PgIAAXLt2DWq12t7hlDq8/vbDa29fvP72VZyvvxACKSkp8PPzg1ye98wp9lQVIblcDn9/f3uH4TDUanWx+49VkvD62w+vvX3x+ttXcb3+j+uhysWJ6kREREQ2wKSKiIiIyAaYVFGRU6lUmDx5MlQqlb1DKZV4/e2H196+eP3tqzRcf05UJyIiIrIB9lQRERER2QCTKiIiIiIbYFJFREREZANMqoiIiIhsgEkVFYmZM2fiueeeg6enJypUqIDw8HBcuHDB3mGVWrNmzYJMJkNkZKS9Qyk1bty4gddeew1ly5aFq6sr6tati2PHjtk7rFLBYDBg4sSJqFy5MlxdXVGlShVMmzbtifu40dPZt28funTpAj8/P8hkMmzcuFFyXAiBSZMmwdfXF66urggNDcXFixftE6yNMamiIrF3714MHz4cf/zxB3bs2IHs7Gy0a9cOaWlp9g6t1Dl69Ci+/PJL1KtXz96hlBpJSUlo0aIFnJ2dsW3bNpw/fx7z5s2Dl5eXvUMrFWbPno3Fixdj4cKFiI2NxezZszFnzhx8/vnn9g6tREpLS8Ozzz6LL774wuLxOXPmYMGCBViyZAkOHz4Md3d3hIWFISMjo4gjtT0uqUB28d9//6FChQrYu3cvWrZsae9wSo3U1FQ0bNgQixYtwvTp01G/fn3Mnz/f3mGVeOPGjcOBAwewf/9+e4dSKnXu3Bne3t74+uuvTWU9evSAq6srvvvuOztGVvLJZDL89NNPCA8PB3C/l8rPzw+jR4/GmDFjAAA6nQ7e3t5YsWIFevfubcdoC449VWQXOp0OAFCmTBk7R1K6DB8+HJ06dUJoaKi9QylVNm3ahMaNG+OVV15BhQoV0KBBA3z11Vf2DqvUaN68OXbu3Im///4bAHDq1Cn8/vvv6NChg50jK33i4uKQkJAg+R2k0WjQpEkTHDp0yI6R2QY3VKYiZzQaERkZiRYtWqBOnTr2DqfU+OGHH3DixAkcPXrU3qGUOv/88w8WL16Md999Fx988AGOHj2KUaNGQalUYsCAAfYOr8QbN24c9Ho9atasCYVCAYPBgBkzZqBfv372Dq3USUhIAAB4e3tLyr29vU3HijMmVVTkhg8fjrNnz+L333+3dyilxrVr1xAREYEdO3bAxcXF3uGUOkajEY0bN8bHH38MAGjQoAHOnj2LJUuWMKkqAuvWrcPq1auxZs0a1K5dGydPnkRkZCT8/Px4/cmmOPxHRWrEiBHYsmULdu/eDX9/f3uHU2ocP34ct27dQsOGDeHk5AQnJyfs3bsXCxYsgJOTEwwGg71DLNF8fX1Rq1YtSVlwcDDi4+PtFFHp8t5772HcuHHo3bs36tati9dffx3vvPMOZs6cae/QSh0fHx8AQGJioqQ8MTHRdKw4Y1JFRUIIgREjRuCnn37Crl27ULlyZXuHVKq0bdsWZ86cwcmTJ02Pxo0bo1+/fjh58iQUCoW9QyzRWrRoYbaEyN9//41KlSrZKaLSJT09HXK59OtOoVDAaDTaKaLSq3LlyvDx8cHOnTtNZXq9HocPH0azZs3sGJltcPiPisTw4cOxZs0a/Pzzz/D09DSNnWs0Gri6uto5upLP09PTbP6au7s7ypYty3ltReCdd95B8+bN8fHHH6NXr144cuQIli5diqVLl9o7tFKhS5cumDFjBgIDA1G7dm38+eef+PTTT/HGG2/YO7QSKTU1FZcuXTI9j4uLw8mTJ1GmTBkEBgYiMjIS06dPR7Vq1VC5cmVMnDgRfn5+pjsEizVBVAQAWHwsX77c3qGVWq1atRIRERH2DqPU2Lx5s6hTp45QqVSiZs2aYunSpfYOqdTQ6/UiIiJCBAYGChcXF/HMM8+ICRMmiMzMTHuHViLt3r3b4u/7AQMGCCGEMBqNYuLEicLb21uoVCrRtm1bceHCBfsGbSNcp4qIiIjIBjinioiIiMgGmFQRERER2QCTKiIiIiIbYFJFREREZANMqoiIiIhsgEkVERERkQ0wqSIiIiKyASZVREQANm7ciKpVq0KhUCAyMtLe4TyVoKAgzJ8/395hEJVaTKqI6KkJIRAaGoqwsDCzY4sWLYJWq8X169ftEFn+vfXWW+jZsyeuXbuGadOmWawTFBQEmUxm9pg1a1YRR2vZ0aNHMXToUHuHQVRqcUV1IiqQa9euoW7dupg9ezbeeustAPf3+qpbty4WL16M119/3abny87OhrOzs03bTE1NhaenJ3bt2oU2bdrkWS8oKAiDBw/GkCFDJOWenp5wd3e3aUz5kZWVBaVSabfzE9F97KkiogIJCAhAVFQUxowZg7i4OAghMHjwYLRr1w4NGjRAhw4d4OHhAW9vb7z++uu4ffu26bUxMTF44YUXoNVqUbZsWXTu3BmXL182Hb9y5QpkMhnWrl2LVq1awcXFBatXr8bVq1fRpUsXeHl5wd3dHbVr18Yvv/ySZ4xJSUno378/vLy84Obmhg4dOuDixYsAgD179sDT0xMA8OKLL0Imk2HPnj15tuXp6QkfHx/JIzehmjp1Kvz8/HDnzh1T/U6dOqFNmzYwGo0AAJlMhsWLF6NDhw5wdXXFM888gw0bNkjOce3aNfTq1QtarRZlypRB165dceXKFdPxgQMHIjw8HDNmzICfnx9q1KgBwHz4Lzk5GW+++SbKly8PtVqNF198EadOnTIdnzJlCurXr49Vq1YhKCgIGo0GvXv3RkpKiqmO0WjEnDlzULVqVahUKgQGBmLGjBlWx0pUmjCpIqICGzBgANq2bYs33ngDCxcuxNmzZ/Hll1/ixRdfRIMGDXDs2DHExMQgMTERvXr1Mr0uLS0N7777Lo4dO4adO3dCLpejW7dupgQk17hx4xAREYHY2FiEhYVh+PDhyMzMxL59+3DmzBnMnj0bHh4eecY3cOBAHDt2DJs2bcKhQ4cghEDHjh2RnZ2N5s2b48KFCwCAH3/8ETdv3kTz5s2f6jpMmDABQUFBePPNNwEAX3zxBQ4ePIiVK1dCLn/w63bixIno0aMHTp06hX79+qF3796IjY0FcL8nLiwsDJ6enti/fz8OHDgADw8PtG/fHllZWaY2du7ciQsXLmDHjh3YsmWLxXheeeUV3Lp1C9u2bcPx48fRsGFDtG3bFnfv3jXVuXz5MjZu3IgtW7Zgy5Yt2Lt3r2Q4c/z48Zg1axYmTpyI8+fPY82aNfD29s5XrESlhh03cyaiEiQxMVGUK1dOyOVy8dNPP4lp06aJdu3aSepcu3ZNAMhzR/r//vtPABBnzpwRQggRFxcnAIj58+dL6tWtW1dMmTLFqrj+/vtvAUAcOHDAVHb79m3h6uoq1q1bJ4QQIikpSQAQu3fvfmxblSpVEkqlUri7u0se+/btM9W5fPmy8PT0FGPHjhWurq5i9erVkjYAiLfffltS1qRJEzFs2DAhhBCrVq0SNWrUEEaj0XQ8MzNTuLq6iu3btwshhBgwYIDw9vYWmZmZZvF99tlnQggh9u/fL9RqtcjIyJDUqVKlivjyyy+FEEJMnjxZuLm5Cb1ebzr+3nvviSZNmgghhNDr9UKlUomvvvrK4vWwJlai0sTJngkdEZUcFSpUwFtvvYWNGzciPDwcq1evxu7duy32IF2+fBnVq1fHxYsXMWnSJBw+fBi3b9829VDFx8ejTp06pvqNGzeWvH7UqFEYNmwYfv31V4SGhqJHjx6oV6+exbhiY2Ph5OSEJk2amMrKli2LGjVqmHqH8uO9997DwIEDJWUVK1Y0/fuZZ57BJ598grfeeguvvvoq+vbta9ZGs2bNzJ6fPHkSAHDq1ClcunTJNCSZKyMjQzI0Wrdu3cfOozp16hRSU1NRtmxZSfm9e/ck7QQFBUnO5evri1u3bgG4f+0yMzPRtm3bPM9hTaxEpQWTKiKyGScnJzg53f+1kpqaii5dumD27Nlm9Xx9fQEAXbp0QaVKlfDVV1/Bz88PRqMRderUMRs6enQS+JtvvomwsDBs3boVv/76K2bOnIl58+Zh5MiRhfTOHihXrhyqVq362Dr79u2DQqHAlStXkJOTY7om1khNTUWjRo2wevVqs2Ply5c3/ftJE+NTU1Ph6+trcX6YVqs1/fvRSf8ymcyU3Lq6utokVqLSgnOqiKhQNGzYEOfOnUNQUBCqVq0qebi7u+POnTu4cOECPvzwQ7Rt2xbBwcFISkqyuv2AgAC8/fbbiI6OxujRo/HVV19ZrBccHIycnBwcPnzYVJZ77lq1ahX4fT5q7dq1iI6Oxp49exAfH29xeYY//vjD7HlwcDCA+9ft4sWLqFChgtl102g0VsfRsGFDJCQkwMnJyaydcuXKWdVGtWrV4Orqip07d+Z5DlvESlRSMKkiokIxfPhw3L17F3369MHRo0dx+fJlbN++HYMGDYLBYICXlxfKli2LpUuX4tKlS9i1axfeffddq9qOjIzE9u3bERcXhxMnTmD37t2mpORR1apVQ9euXTFkyBD8/vvvOHXqFF577TVUrFgRXbt2zff7SklJQUJCguSh1+sBANevX8ewYcMwe/ZsvPDCC1i+fDk+/vhjsyRq/fr1+Oabb/D3339j8uTJOHLkCEaMGAEA6NevH8qVK4euXbti//79iIuLw549ezBq1Kh8rfkVGhqKZs2aITw8HL/++iuuXLmCgwcPYsKECTh27JhVbbi4uGDs2LF4//338e233+Ly5cv4448/8PXXX9s0VqKSgkkVERUKPz8/HDhwAAaDAe3atUPdunURGRkJrVYLuVwOuVyOH374AcePH0edOnXwzjvvYO7cuVa1bTAYMHz4cAQHB6N9+/aoXr06Fi1alGf95cuXo1GjRujcuTOaNWsGIQR++eWXp1rvatKkSfD19ZU83n//fQghMHDgQDz//POmBCksLAzDhg3Da6+9htTUVFMbH330EX744QfUq1cP3377Lb7//ntTr5mbmxv27duHwMBAdO/eHcHBwRg8eDAyMjKgVqutjlMmk+GXX35By5YtMWjQIFSvXh29e/fG1atXTXfvWWPixIkYPXo0Jk2ahODgYLz66qumOVe2ipWopODin0RERUgmk+Gnn35CeHi4vUMhIhtjTxURERGRDTCpIiIiIrIBLqlARFSEOOOCqORiTxURERGRDTCpIiIiIrIBJlVERERENsCkioiIiMgGmFQRERER2QCTKiIiIiIbYFJFREREZANMqoiIiIhsgEkVERERkQ38H6QwYR0cA01nAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualising the Test set results"
      ],
      "metadata": {
        "id": "JPX02k16Fwiq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the test data points and the linear regression line\n",
        "plt.scatter(x_test, y_test, color=\"brown\", label=\"Actual Test Data\")\n",
        "plt.plot(x_train, x_pred, color=\"skyblue\", label=\"Linear Regression Line\")\n",
        "plt.title(\"Salary vs Experience (Test Dataset)\")\n",
        "plt.xlabel(\"Years of Experience\")\n",
        "plt.ylabel(\"Salary (in MUR)\")\n",
        "plt.legend()  # Show legend for clarity\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "5XkrBnhR5si4",
        "outputId": "aa7a23a9-04c8-4416-cc55-7d2cd2d60b13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}